Artificial syntactic violations activate Broca's region

Petersson, Karl Magnus; Forkstam, Christian; Ingvar, Martin

doi:10.1207/s15516709cog2803_4

Cited by 107 publications

(232 citation statements)

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“…Here we only give a brief summary of the most important results. Consistent with previous findings (Forkstam et al, 2006;Petersson et al, 2004) the overall correct classification performance was clearly above chance (73 ± 16% correct, T(31) = 7.7, P < .001). The analysis of hit rate showed that the subjects were sensitive to the grammaticality of the items (F(2, 62) = 26, P < .001).…”

Section: Behavioral Characterizationsupporting

confidence: 91%

“…The size of this region was chosen because we used a spatial filter kernel of FWHM = 10 mm, which roughly corresponds to the spatial scale of localization precision in group FMRI studies (Brett, Johnsrude, & Owen, 2002;Petersson, Nichols, Poline, & Holmes, 1999), including language (Hagoort, 2005;Petersson et al, 2004). In this analysis we used small volume correction based on the family-wise error (FWE) corrected for multiple non-independent comparisons based on smooth 3D random field theory (Adler & Taylor, 2007).…”

Section: Mr Image Preprocessing and Statistical Analysismentioning

confidence: 99%

“…In the standard version, subjects are informed after acquisition that the sequences were generated according to a complex set of rules, and are asked to classify novel sequences as grammatical or not, based on their immediate intuitive impression (i.e., guessing based on ''gut feeling"). A robust finding in this type of paradigm is that after several days of implicit acquisition subjects perform well above chance; they do so on regular (e.g., Folia et al, 2008;Forkstam, Elwér, Ingvar, & Petersson, 2008;Petersson et al, 2004;Stadler & Frensch, 1998) as well as non-regular grammars (Poletiek, 2002;Uddén et al, 2009), generating context-free and context-sensitive non-adjacent dependencies (Uddén, Ingvar, Hagoort, & Petersson, submitted for publication; Uddén et al, 2009). In passing, we note that a qualitative match between the performance of simple recurrent networks and human comprehension of nested (context-free) and crossed (context-sensitive) dependencies has been reported (Christiansen & Chater, 1999;Christiansen & MacDonald, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, natural language data was acquired in the same subjects in another FMRI experiment, very similar to the artificial grammar experiment, in a 2 Â 2 factorial design with the factors syntax and semantics (Folia, Forkstam, Hagoort, & Petersson, 2009). Based on previous findings (Forkstam et al, 2006;Petersson et al, 2004;Uddén et al, 2008), we predicted that artificial syntax processing would engage the left inferior frontal region (Brodmann's area (BA) 44 and 45) and that this activation would overlap with syntax-related variability observed in the natural language experiment.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore the artificial grammar learning (AGL) paradigm has been used to create a relatively uncontaminated window onto the neurobiology of syntax (Gómez & Gerken, 2000;Petersson, Forkstam, & Ingvar, 2004;Reber, 1967). In addition, AGL has been used in cross-species comparisons in an attempt to establish the uniquely human component of language (Fitch & Hauser, 2004;Gentner, Fenn, Margoliash, & Nusbaum, 2006;Hauser, Chomsky, & Fitch, 2002;O'Donnell, Hauser, & Fitch, 2005;Saffran et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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What artificial grammar learning reveals about the neurobiology of syntax

2012

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a b s t r a c tIn this paper we examine the neurobiological correlates of syntax, the processing of structured sequences, by comparing FMRI results on artificial and natural language syntax. We discuss these and similar findings in the context of formal language and computability theory. We used a simple right-linear unification grammar in an implicit artificial grammar learning paradigm in 32 healthy Dutch university students (natural language FMRI data were already acquired for these participants). We predicted that artificial syntax processing would engage the left inferior frontal region (BA 44/45) and that this activation would overlap with syntax-related variability observed in the natural language experiment. The main findings of this study show that the left inferior frontal region centered on BA 44/45 is active during artificial syntax processing of well-formed (grammatical) sequence independent of local subsequence familiarity. The same region is engaged to a greater extent when a syntactic violation is present and structural unification becomes difficult or impossible. The effects related to artificial syntax in the left inferior frontal region (BA 44/45) were essentially identical when we masked these with activity related to natural syntax in the same subjects. Finally, the medial temporal lobe was deactivated during this operation, consistent with the view that implicit processing does not rely on declarative memory mechanisms that engage the medial temporal lobe. In the context of recent FMRI findings, we raise the question whether Broca's region (or subregions) is specifically related to syntactic movement operations or the processing of hierarchically nested non-adjacent dependencies in the discussion section. We conclude that this is not the case. Instead, we argue that the left inferior frontal region is a generic on-line sequence processor that unifies information from various sources in an incremental and recursive manner, independent of whether there are any processing requirements related to syntactic movement or hierarchically nested structures. In addition, we argue that the Chomsky hierarchy is not directly relevant for neurobiological systems.

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Section: Behavioral Characterizationsupporting

confidence: 91%

Section: Mr Image Preprocessing and Statistical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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What artificial grammar learning reveals about the neurobiology of syntax

2012

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Deficits in statistical leaning of auditory sequences among adults with dyslexia

Kahta

Schiff

2019

Dyslexia

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Recently, it has been suggested that developmental dyslexia (DD) is related to deficits in general mechanisms of statistical learning (SL). The aim of the current study was to explore these relations using a nonlinguistic auditory artificial grammar learning (A‐AGL) task. Most studies using AGL to explore the role of SL among readers with dyslexia used visual stimuli. The current study explored SL abilities among adults with DD using a nonlinguistic auditory task, because evidence suggests that SL is affected by the modality of stimuli. Forty‐eight (21 DD and 27 typically developed [TD]) adults participated in two A‐AGL tasks: implicit and explicit. The results showed a significant difference between the groups, as TD readers outperformed adults with DD. This difference in performance supports the SL deficit hypothesis among adults with dyslexia, although the causal relations between auditory SL and reading still require further examination. In addition, no difference was found between the implicit and explicit tasks, suggesting that unlike the visual AGL, participants with DD do not benefit from elevating attentional resources during A‐AGL.

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Project DyAdd: Implicit learning in adult dyslexia and ADHD

et al. 2013

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In this study of the project DyAdd, implicit learning was investigated through two paradigms in adults (18-55 years) with dyslexia (n = 36) or with attention deficit/hyperactivity disorder (ADHD, n = 22) and in controls (n = 35). In the serial reaction time (SRT) task, there were no group differences in learning. However, those with ADHD exhibited faster RTs compared to other groups. In the artificial grammar learning (AGL) task, the groups did not differ from each other in their learning (i.e., grammaticality accuracy or similarity choices). Further, all three groups were sensitive to fragment overlap between learning and test-phase items (i.e., similarity choices were above chance). Grammaticality performance of control participants was above chance, but that of participants with dyslexia and participants with ADHD failed to differ from chance, indicating impaired grammaticality learning in these groups. While the main indices of AGL performance, grammaticality accuracy and similarity choices did not correlate with the neuropsychological variables that reflected dyslexia-related (phonological processing, reading, spelling, arithmetic) or ADHD-related characteristics (executive functions, attention), or intelligence, the explicit knowledge for the AGL grammar (i.e., ability to freely generate grammatical strings) correlated positively with the variables of phonological processing and reading. Further, SRT reaction times correlated positively with full scale intelligence quotient (FIQ). We conclude that, in AGL, learning difficulties of the underlying rule structure (as measured by grammaticality) are associated with dyslexia and ADHD. However, learning in AGL is not related to the defining neuropsychological features of dyslexia or ADHD. Instead, the resulting explicit knowledge relates to characteristics of dyslexia.

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Artificial syntactic violations activate Broca's region

Cited by 107 publications

References 123 publications

What artificial grammar learning reveals about the neurobiology of syntax

What artificial grammar learning reveals about the neurobiology of syntax

Deficits in statistical leaning of auditory sequences among adults with dyslexia

Project DyAdd: Implicit learning in adult dyslexia and ADHD

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